Dynamic XQuery Generation for Non-XML Data Retrieval
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Solution Overview
Problem
Existing XQuery Builders are not suitable for searching large amounts of unknown and un-categorized data, particularly text data, as they require a high level of user skill and are not flexible enough to handle non-XML documents effectively.
Innovation Solution
A method for dynamically generating XQuery by providing static units of XQuery code that can be combined based on user input, using a GUI to facilitate the creation of queries for retrieving non-XML documents and their corresponding shadow XML documents, incorporating predefined FLWOR expressions with dynamic user criteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If XQuery Builders provide comprehensive query functionality, then query power and versatility are improved, but user skill requirement and operational complexity increase
Solution Approach 1:
The XQuery Builder divides the query construction process into discrete, manageable steps: selecting shadow XML documents, choosing retrieval criteria, specifying search terms, and configuring parameters. Each step presents only relevant options to the user, breaking down the complex task of building an XQuery into sequential, easy-to-follow stages that reduce cognitive load and skill requirements.
Solution Approach 2:
The graphical user interface acts as an intermediary between the user and the XQuery language syntax. Instead of requiring users to write complex XQuery code directly, the GUI provides visual forms, dropdown menus, and wizards that translate user selections into properly formatted XQuery statements, shielding users from syntax complexity while maintaining full query functionality.
2Ease of operation
If XQuery Builders simplify the interface, then ease of operation is improved, but functionality and adaptability are reduced
Solution Approach 1:
The XQuery Builder dynamically adapts its interface based on user selections and query context. The wizard presents different options and fields depending on what the user has already chosen, showing only relevant criteria and parameters for each specific query type. This dynamic behavior maintains simplicity by hiding irrelevant options while preserving versatility by making all necessary options available when needed.
Solution Approach 2:
The graphical interface is designed to handle multiple query types and retrieval scenarios through a unified workflow. The same basic interface structure supports different shadow XML document selections, various retrieval criteria (text search, metadata filtering, date ranges), and multiple search term options, allowing a single simplified interface to provide comprehensive adaptability across diverse query needs.
3Manufacturing precision
If the system requires users to write custom XQuery code, then query precision and control are improved, but productivity and ease of use deteriorate
Solution Approach 1:
The system pre-defines common retrieval criteria, search term patterns, and parameter configurations based on typical query requirements. The wizard provides pre-configured options for text search, metadata filtering, and date range queries, allowing users to quickly assemble precise queries by selecting and combining these pre-prepared building blocks rather than constructing everything from scratch, thus maintaining precision while accelerating query creation.
Data Source
AI summary
Dynamically generating an XQuery for an XML database storing a plurality of non-XML documents, each non-XML document having a corresponding shadow XML document in the XML database. The method may include providing a plurality of static units of XQuery code, the static units being predefined in accordance with the non-XML documents. The method may further include combining the one or more static units with dynamic input from a user to generate the XQuery.